{"id":"W7100456879","doi":"","title":"Face recognition with weighted locally linear embedding, in: The Second Canadian Conference on Computer and Robot Vision","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Facial recognition system; Nonlinear dimensionality reduction; Face (sociological concept); Dimensionality reduction; Embedding; Principal component analysis; Robot; Pattern recognition (psychology); Software","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009033775,0.0007617758,0.0007123757,0.001278756,0.0003672087,0.001143589,0.001128769,0.0007349752,0.006836318],"category_scores_gemma":[0.001474787,0.0003223873,0.0003794638,0.001057576,0.0007138147,0.002070566,0.000740556,0.0005545526,0.002182263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005519926,"about_ca_system_score_gemma":0.0008025767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0109301,"about_ca_topic_score_gemma":0.01864558,"domain_scores_codex":[0.9996033,0.00007122977,0.00002305791,0.0001008662,0.0001550339,0.00004641122],"domain_scores_gemma":[0.9996237,0.00007949456,0.00002741072,0.00007063761,0.0001690376,0.00002970699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001781145,0.00008398052,0.0007136948,0.000157401,0.00007400809,0.0001255469,0.00008971742,0.01231958,0.01869396,0.003744031,0.05512509,0.9086949],"study_design_scores_gemma":[0.00006305233,0.0002924945,0.0066015,0.00010115,0.0001689132,0.001331306,0.0002859563,0.7998203,0.04700119,0.02654705,0.1176549,0.0001321421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01876342,0.01231339,0.9573781,0.001158097,0.001334256,0.000116582,0.000318397,0.003502785,0.005115042],"genre_scores_gemma":[0.208928,0.01017436,0.7358686,0.0005266804,0.001075468,0.0001770225,0.001950678,0.0006269477,0.04067224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0109301,"threshold_uncertainty_score":0.02286977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952858874647458,"score_gpt":0.2502661119733148,"score_spread":0.2307375232268403,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}